Magnetic resonance imaging apparatus and method

ABSTRACT

Disclosed are a magnetic resonance imaging (MRI) apparatus and method. The MRI apparatus includes a data acquirer, which performs under-sampling of MR signals, respectively received from a plurality of channel coils included in a radio frequency (RF) multi-coil, at non-uniform intervals to acquire a plurality of pieces of line data, and an image processor that restores a plurality of pieces of K-space data respectively corresponding to the plurality of channel coils by using a relationship between the acquired plurality of pieces of line data, thereby restoring an MR image with reduced aliasing artifacts.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from Korean Patent Application No.10-2013-0126726, filed on Oct. 23, 2013 and Korean Patent ApplicationNo. 10-2014-0143408, filed on Oct. 22, 2014, in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein in itsentirety by reference.

BACKGROUND

1. Field

One or more embodiments of the present invention relate to a magneticresonance imaging (MRI) apparatus and method, and more particularly, toan MRI apparatus and method that perform under-sampling on a pluralityof pieces of K-space data acquired by a radio frequency (RF) multi-coilto acquire an MR image.

2. Description of the Related Art

A magnetic resonance imaging (MRI) apparatus is an apparatus thatphotographs an object by using a magnetic field. An MRI apparatus iscapable of illustrating lumbar discs, joints, and nerve ligaments, inaddition to bones, three-dimensionally at a desired angle. Theseapparatuses are therefore widely used for an accurate diagnosis of adisease.

An MRI apparatus acquires a magnetic resonance (MR) signal, reconfiguresthe acquired MR signal into an image, and outputs the image.Specifically, an MRI apparatus acquires the MR signal by using radiofrequency (RF) coils, a permanent magnet, and a gradient coil.

Specifically, by using a pulse sequence used to generate an RF signal,an MRI apparatus applies the RF signal to an object through an RFmulti-coil, and performs sampling on an MR signal generated in responseto the applied RF signal to restore an MR image.

At present, about one hour is used for capturing an MR image. Generally,the MRI apparatus is implemented as a long and narrow tube (hereinafterreferred to as an MRI tube). Therefore, a patient to be photographed forobtaining an MR image enters an MRI tube, and should not move whilephotographing. Due to this, it is difficult to capture an MR image of acritical patient or a claustrophobe, and moreover, even for generalpatients, a photographing time becomes longer, causing boredom andinconvenience.

Therefore, an image processing apparatus and method for shortening acapturing time of an MR image are needed.

A method, which does not perform sampling of an MR signal for all linesof a K-space image but performs under-sampling that performs sampling ofthe MR signal at some line intervals of the K-space image, andcalibrates the under-sampled K-space data to perform imaging on a finalMR image, may be used to shorten a capturing time of an MR image.

Specifically, a generalized auto-calibrating partially parallelacquisition (GRAPPA) method, which is an example of a k-space-basedimaging method, performs self-calibration to calculate a spatialcorrelation or convolution kernel coefficient that is a spatialinteraction value between a calibration signal and a measured sourcesignal adjacent thereto, and estimates an unmeasured signal by using thecalculated spatial correlation coefficient.

In detail, the GRAPPA method restores unobtained k-space lines bychannel by using a measured signal that is under-sampled data andadditionally acquired auto-calibration signal (ACS) line data.

In restoring k-space data by performing calibration, when data of animage signal is damaged or a spatial interaction value is changed due tonoise, aliasing artifacts and amplified noise of a finally acquired MRimage occur.

Therefore, it is required to provide an imaging method and apparatusthat decreases the number of aliasing artifacts, and restores an MRimage with an improved quality by removing amplified noise.

However, due to tradeoff, it is difficult to satisfy all theabove-described requirements for reducing a time taken in capturing anMR image and for improving a quality of a restored MR image.

SUMMARY

One or more embodiments of the present invention include an MRIapparatus and method, which improve a quality of a restored MR image.

One or more embodiments of the present invention include an MRIapparatus and method, which prevent a quality of an image from beingdegraded due to aliasing artifacts, thus improving a quality of arestored MR image.

One or more embodiments of the present invention include an MRIapparatus and method, which acquire K-space data through under-sampling,thus quickly acquiring an MR image.

One or more embodiments of the present invention include an MRIapparatus and method, which quickly acquire an MR image with an improvedquality even without using an additional calibration signal, which isacquired from a partial region of a K-space and used in a GRAPPAtechnique or using a coil sensitivity map that has additional coilinformation and is used in a simultaneous acquisition of spatialharmonics (SMASH) technique.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

According to one or more embodiments of the present invention, amagnetic resonance imaging (MRI) apparatus includes: a data acquirerthat performs under-sampling of magnetic resonance (MR) signals,respectively received from a plurality of channel coils included in aradio frequency (RF) multi-coil, at non-uniform intervals to acquire aplurality of pieces of line data; and an image processor that restores aplurality of pieces of K-space data respectively corresponding to theplurality of channel coils by using a relationship between the acquiredplurality of pieces of line data.

The data acquirer may perform under-sampling of the MR signals atnon-uniform intervals in a whole K-space corresponding to each of theplurality of channel coils.

The data acquirer may divide a K-space corresponding to one channel coilinto a plurality of blocks, and performs under-sampling of acorresponding MR signal for each of the plurality of blocks atnon-uniform intervals to acquire the plurality of pieces of line data.

A non-uniform under-sampling pattern, which is generated based on thepieces of line data which are acquired by performing the under-samplingat non-uniform intervals, may be the same in the plurality of blocks.

The plurality of blocks may include the same under-sampling intervalpattern.

By using the relationship between the acquired plurality of pieces ofline data, the image processor may restore a plurality of pieces ofunacquired line data, and by using at least one of the restoredplurality of pieces of line data and the acquired plurality of pieces ofline data, the image processor may restore K-space data corresponding tothe one channel coil.

The image processor may set one of the acquired plurality of pieces ofline data to reference line data, and when spatial distances between thereference line data and at least two of the acquired plurality of piecesof line data have a first relationship, the image processor may restorethe plurality of pieces of unacquired line data by using the firstrelationship.

The image processor may restore unacquired line data having the firstrelationship with at least two of the acquired plurality of pieces ofline data, and restore unacquired line data having the firstrelationship with at least one of the restored plurality of pieces ofline data and at least one of the acquired plurality of pieces of linedata.

By using the reference line data and at least two of the acquiredplurality of pieces of line data, the image processor may calculate aspatial correlation coefficient corresponding to the first relationship,and by using the spatial correlation coefficient, the image processormay restore unacquired line data.

The image processor may sequentially restore a plurality of pieces ofunacquired line data, included in each of the plurality of blocks, in apredetermined order.

A plurality of non-uniform under-sampling patterns, which are generatedbased on the pieces of line data which are acquired by performing theunder-sampling at non-uniform intervals, may differ in the plurality ofblocks.

The plurality of blocks may be divided into a plurality of groups, andthe plurality of groups may include different under-sampling intervalpatterns.

The plurality of blocks may include at least one first block and atleast one second block, and the first and second blocks may includedifferent under-sampling interval patterns.

The first block may include more or less of the acquired plurality ofpieces of line data than the second block.

The plurality of blocks may include at least one first block, at leastone second block, and at least one third block, the second block mayinclude less of the acquired plurality of pieces of line data than thefirst block, and the third block may include more of the acquiredplurality of pieces of line data than the first block.

The third block may be disposed closer to a center line of the K-spacethan the first and second blocks.

The image processor may perform a spatial transform on the restoredplurality of pieces of K-space data to generate a plurality of MR imagesby channel, and acquire a final MR image by using the plurality of MRimages by channel.

The image processor may perform an inverse Fourier transform on therestored plurality of pieces of K-space data to generate the pluralityof MR images by channel.

The image processor may calculate a sum of squares or complex sum of theplurality of MR images by channel to generate the final MR image.

The number of the blocks and a size of each of the blocks may be setbased on at least one selected from a hardware type of the RF multi-coiland a part of an object to be photographed.

A non-uniform under-sampling pattern, which is generated based on thepieces of line data which are acquired by performing the under-samplingat non-uniform intervals, may be set based on at least one selected froma hardware type of the RF multi-coil and a part of an object to bephotographed.

According to one or more embodiments of the present invention, amagnetic resonance imaging (MRI) apparatus includes: a data acquirerthat performs under-sampling on magnetic resonance (MR) signals,respectively received from a plurality of channel coils included in aradio frequency (RF) multi-coil, at non-uniform intervals in a wholeK-space corresponding to one of the channel coils to acquire a pluralityof pieces of line data; and an image processor that restores a pluralityof pieces of K-space data respectively corresponding to the plurality ofchannel coils on a basis of a spatial correlation coefficient which iscalculated by using at least one of a plurality of pieces of line dataacquired in a center region of the K-space and at least one of aplurality of pieces of line data acquired in a peripheral region of theK-space.

The data acquirer may divide the K-space into a plurality of blocks, andset, as a calibration block, a first block which is disposed in thecenter region of the K-space.

The data acquirer may perform sampling on all lines in the calibrationblock to acquire a plurality of pieces of calibration line data, thedata acquirer may perform under-sampling at first intervals to acquire aplurality of pieces of first line data in the whole K-space, and thedata acquirer may additionally perform sampling on at least one secondline to further acquire at least one piece of second line data in thesecond block disposed in the peripheral region of the K-space.

The image processor may acquire a spatial correlation coefficient byusing the plurality of pieces of calibration line data, the plurality ofpieces of first line data, and the at least one piece of second linedata.

According to one or more embodiments of the present invention, amagnetic resonance imaging (MRI) apparatus includes: a data acquirerthat performs under-sampling of magnetic resonance (MR) signals, whichare respectively received from a plurality of channel coils included ina radio frequency (RF) multi-coil, at non-uniform intervals in a wholeK-space corresponding to each of the plurality of channel coils toacquire pieces of line data; and an image processor that restorescomplete K-space data corresponding to each of the plurality of channelcoils by using a relationship between the pieces of line data which areacquired by performing the under-sampling at non-uniform intervals.

According to one or more embodiments of the present invention, a MRimaging method using a radio frequency (RF) multi-coil, including aplurality of channel coils, includes: performing under-sampling of MRsignals, respectively received from the plurality of channel coils, atnon-uniform intervals to acquire a plurality of pieces of line data; andrestoring a plurality of pieces of K-space data respectivelycorresponding to the plurality of channel coils by using a relationshipbetween the acquired plurality of pieces of line data.

The acquiring of the pieces of line data may include performingunder-sampling of the MR signals at non-uniform intervals in a wholeK-space corresponding to each of the plurality of channel coils toacquire the pieces of line data.

The acquiring of a plurality of pieces of line data may include dividinga K-space corresponding to each of the plurality of channel coils into aplurality of blocks, and performing under-sampling of a corresponding MRsignal for each of the plurality of blocks at non-uniform intervals toacquire the plurality of pieces of line data.

A non-uniform under-sampling pattern, which is generated based on thepieces of line data which are acquired by performing the under-samplingat non-uniform intervals, may be the same in the plurality of blocks.

The restoring of a plurality of pieces of K-space data may include:restoring a plurality of pieces of unacquired line data by using therelationship between the acquired plurality of pieces of line data; andrestoring K-space data corresponding to the one channel coil by using atleast one of the restored plurality of pieces of line data and theacquired plurality of pieces of line data.

The restoring of a plurality of pieces of K-space data may include:setting one of the acquired plurality of pieces of line data toreference line data; and when spatial distances between the referenceline data and at least two of the acquired plurality of pieces of linedata have a first relationship, restoring the plurality of pieces ofunacquired line data by using the first relationship.

The restoring of the plurality of pieces of unacquired line data mayinclude: restoring unacquired line data having the first relationshipwith at least two of the acquired plurality of pieces of line data; andrestoring unacquired line data having the first relationship with atleast one of the restored plurality of pieces of line data and theacquired plurality of pieces of line data.

The restoring of the plurality of pieces of unacquired line data mayinclude: calculating a spatial correlation coefficient corresponding tothe first relationship by using the reference line data and at least twoof the acquired plurality of pieces of line data; and restoringunacquired line data by using the spatial correlation coefficient.

The restoring of the plurality of pieces of unacquired line data mayinclude sequentially restoring a plurality of pieces of unacquired linedata, included in each of the plurality of blocks, in a predeterminedorder.

A plurality of non-uniform under-sampling patterns, which are generatedbased on the pieces of line data which are acquired by performing theunder-sampling at non-uniform intervals, may differ in the plurality ofblocks.

The plurality of blocks may include at least one first block and atleast one second block, and the first and second blocks may includedifferent under-sampling interval patterns.

The first block may include more than or less of the acquired pluralityof pieces of line data than the second block.

The plurality of blocks may include at least one first block, at leastone second block, and at least one third block, and the first to thirdblocks may include different under-sampling interval patterns.

The second block may include less of the acquired plurality of pieces ofline data than the first block, and the third block may include more ofthe acquired plurality of pieces of line data than the first block.

The third block may be disposed closer to a center line of the K-spacethan the first and second blocks.

The method may further include: performing a spatial transform on therestored plurality of pieces of K-space data to generate a plurality ofMR images by channel; and acquiring a final MR image by using theplurality of MR images by channel.

The generating of a plurality of MR images may include performing aninverse Fourier transform on the restored plurality of pieces of K-spacedata to generate the plurality of MR images by channel.

The acquiring of a final MR image may include calculating a sum ofsquares or complex sum of the plurality of MR images by channel togenerate the final MR image.

The number of the blocks and a size of each of the blocks may be setbased on at least one selected from a hardware type of the RF multi-coiland a part of an object to be photographed.

A non-uniform under-sampling pattern, which is generated based on thepieces of line data which are acquired by performing the under-samplingat non-uniform intervals, may be set based on at least one selected froma hardware type of the RF multi-coil and a part of an object to bephotographed.

According to one or more embodiments of the present invention, a methodof acquiring a magnetic resonance (MR) image by using a radio frequency(RF) multi-coil including a plurality of channel coils includes:performing under-sampling of MR signals, which are respectively receivedfrom the plurality of channel coils, at non-uniform intervals in a wholeK-space corresponding to each of the plurality of channel coils toacquire pieces of line data; and restoring complete K-space datacorresponding to each of the plurality of channel coils by using arelationship between the pieces of line data which are acquired byperforming the under-sampling at non-uniform intervals.

According to one or more embodiments of the present invention, a methodof acquiring a magnetic resonance (MR) image by using a radio frequency(RF) multi-coil including a plurality of channel coils includes:performing under-sampling of MR signals, which are respectively receivedfrom the plurality of channel coils, at non-uniform intervals in a wholeK-space corresponding to each of the plurality of channel coils toacquire pieces of line data; and restoring pieces of K-space datarespectively corresponding to the plurality of channel coils, based on aspatial correlation coefficient which is calculated by using at leastone of pieces of line data acquired in a center region of the K-spaceand at least one of pieces of line data acquired in a peripheral regionof the K-space.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of the embodiments, taken inconjunction with the accompanying drawings in which:

FIG. 1 is a schematic diagram illustrating a general MRI system;

FIG. 2 is a diagram illustrating an MRI apparatus according to anembodiment of the present invention;

FIGS. 3A and 3B are diagrams for describing an operation of the MRIapparatus according to an embodiment of the present invention;

FIG. 4A is a diagram for describing an operation of the MRI apparatusaccording to an embodiment of the present invention;

FIG. 4B is a diagram for describing a weighting matrix;

FIG. 5 is another diagram for describing an operation of the MRIapparatus according to an embodiment of the present invention;

FIG. 6 is a flowchart of an MR imaging method according to an embodimentof the present invention;

FIG. 7 is a flowchart of an MR imaging method according to anotherembodiment of the present invention;

FIG. 8 are images for describing an MR image generated by the MRIapparatus and the MR imaging method according to an embodiment oranother embodiment of the present invention;

FIG. 9 are images for describing an MR image generated by the MRIapparatus and the MR imaging method according to an embodiment oranother embodiment of the present invention;

FIG. 10 are images for describing an improved quality of an MR imagegenerated by the MRI apparatus and the MR imaging method according to anembodiment or another embodiment of the present invention;

FIG. 11 is a diagram for describing an MRI apparatus according toanother embodiment of the present invention; and

FIG. 12 is a diagram for describing an operation of acquiring a spatialcorrelation coefficient in the MRI apparatus according to anotherembodiment of the present invention.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings, wherein like referencenumerals refer to the like elements throughout. In this regard, thepresent embodiments may have different forms and should not be construedas being limited to the descriptions set forth herein. Accordingly, theembodiments are merely described below, by referring to the figures, toexplain aspects of the present description. Expressions such as “atleast one of,” when preceding a list of elements, modify the entire listof elements and do not modify the individual elements of the list.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings, wherein like referencenumerals refer to like elements throughout. In this regard, the presentembodiments may have different forms and should not be construed asbeing limited to the descriptions set forth herein. Accordingly, theembodiments are merely described below, by referring to the figures, toexplain aspects of the present description. Expressions such as “atleast one of,” when preceding a list of elements, modify the entire listof elements and do not modify the individual elements of the list.

The advantages, features and aspects of the present invention willbecome apparent from the following description of the embodiments withreference to the accompanying drawings, which is set forth hereinafter.The present invention may, however, be embodied in different forms andshould not be construed as limited to the embodiments set forth herein.Rather, these embodiments are provided so that this disclosure will bethorough and complete, and will fully convey the scope of the presentinvention to those skilled in the art.

Terms used herein will be briefly described, and the present inventionwill be described in detail.

Terms used in the present invention have been selected as general termswhich are widely used at present, in consideration of the functions ofthe present invention, but may be altered according to the intent of aperson skilled in the art, conventional practice, or introduction of newtechnology. Also, if there is a term which is arbitrarily selected bythe applicant in a specific case, the meaning thereof will be describedin detail in a corresponding description portion of the presentinvention. Therefore, the terms should be defined on the basis of theentire content of this specification instead of a simple name of each ofthe terms.

In this disclosure below, when it is described that one comprises (orincludes) or when it is described that one comprises (or includes orhas) some elements, it should be understood that it may comprise (orinclude or has) only those elements, or it may comprise (or include orhave) other elements as well if there is no specific limitation. Theterm “module”, as used herein, means, but is not limited to, a softwareor hardware component, such as a Field Programmable Gate Array (FPGA) oran Application Specific Integrated Circuit (ASIC), which performscertain tasks. A module may advantageously be configured to reside inthe addressable storage medium and may be configured to be executed onone or more processors. Thus, a module may include, by way of example,components, such as software components, object-oriented softwarecomponents, class components and task components, processes, functions,attributes, procedures, subroutines, segments of program code, drivers,firmware, microcode, circuitry, data, databases, data structures,tables, arrays, and variables. The functionality provided for in thecomponents and modules may be combined into fewer components and modulesor further separated into additional components and modules.

Exemplary embodiments of the present invention that are capable of beingeasily embodied by those skilled in the art will now be described indetail with reference to the accompanying drawings. In the accompanyingdrawings, a portion irrelevant to a description of the present inventionwill be omitted for clarity.

The term “image” used herein may denote multi-dimensional data composedof discrete image factors (for example, pixels in a two-dimensional (2D)image and pixels in a three-dimensional (3D) image). For example, animage may include a medical image of an object which is acquired by anX-ray apparatus, a computed tomography (CT) apparatus, a magneticresonance imaging (MRI) apparatus, an ultrasonic apparatus, or anothermedical image photographing apparatus.

Moreover, the term “object” used herein may include a person, an animal,a part of the person, or a part of the animal. For example, an objectmay include an organ such as a liver, a heart, a womb, a brain, breasts,an abdomen, or the like, or a blood vessel. Also, the term “object” mayinclude a phantom. The phantom denotes a material having a volume thatis very close to a density of organisms and an effective atomic number,and may include a spherical phantom having a temper similar to a humanbody.

Moreover, the term “user” used herein is a medical expert, and may be adoctor, a nurse, a medical technologist, a medical image expert, or thelike, or may be an engineer repairing a medical apparatus. However, theuser is not limited thereto.

Moreover, the wording “MRI image” used herein denotes an image of anobject which is obtained by using the nuclear magnetic resonanceprinciple.

Moreover, the term “pulse sequence” used herein denotes a continuationof a repeatedly applied signal in an MRI apparatus. The pulse sequencemay include a time parameter of a radio frequency (RF) pulse, forexample, a repetition time (TR) and a time of echo (TE).

An MRI system is equipment that expresses an intensity of a magneticresonance (MR) signal in response to an applied RF signal. The MR signalis generated in a magnetic field having a specific intensity as acontrast, and thus an image of a tomographic part of an object can beobtained. For example, an object is laid in a strong magnetic field.When the RF signal, which only resonates a specific atomic nucleus (forexample, a hydrogen atomic nucleus or the like) that is subjected to aparticular static magnetic field, is irradiated on the object and thenstopped, an MR signal is emitted from the specific atomic nucleus. Inthis case, the MRI system receives the MR signal to obtain an MR image.The MR signal denotes an RF signal emitted from the object. A level ofthe MR signal may be decided by a concentration of a specific atom (forexample, hydrogen or the like) of the object, a relaxation time T1, arelaxation time T2, and blood flow.

An MRI system has different features compared to other imagingapparatuses. Unlike imaging apparatuses such as CT apparatuses, in whichacquisition of an image is dependent on a direction of detectionhardware, the MRI system may acquire a two-dimensional (2D) image or athree-dimensional (3D) volume image which is oriented to an arbitrarypoint. Also, unlike CT apparatuses, X-ray apparatuses, position emissiontomography (PET) apparatuses, and SPECT apparatuses, the MRI system mayacquire an image having high soft tissue without exposing an object todangerous radiation, and thus acquire a neurological image, anintravascular image, a musculoskeletal image, and an oncologic imagewhich need a clear description of an abnormal tissue.

FIG. 1 is a schematic diagram illustrating a general MRI system.Referring to FIG. 1, the general MRI system may include a gantry 20, asignal data acquirer 30, i.e., a signal transceiver, a monitor 40, i.e.,a monitoring unit, a system controller 50, i.e., a system control unit,and an operating unit 60.

The gantry 20 prevents or limits an electromagnetic wave, generated by amain magnet 22, a gradient coil 24, and/or a radio frequency (RF) coil26, from being emitted to the outside. A static electromagnetic fieldand a gradient magnetic field are generated at a bore of the gantry 20,and an RF signal is irradiated toward an object 10 and a MR signal isreceived from object 10 using the RF coil 26.

The main magnet 22, the gradient coil 24, and the RF coil 26 may bedisposed along a certain direction of the gantry 20. The certaindirection may include a coaxial cylinder direction. The object 10 may belocated on a table 28, which is insertable into a cylinder along ahorizontal axis of the cylinder.

The main magnet 22 generates a static magnetic field for aligningmagnetic dipole moments of atomic nucleuses of the object 10 in a givendirection. As the static magnetic field generated by the main magnet 22becomes stronger and more uniform, a more precise and accurate MR imageof the object 10 is acquired.

The gradient coil 24 includes X, Y, and Z coils that respectivelygenerate gradient magnetic fields in X-axis, Y-axis, and Z-axisdirections that are orthogonal to each other. The gradient coil 24 mayinduce different resonance frequencies for each part of the object 10,and provide position information of each part of the object 10.

The RF coil 26 may irradiate an RF signal on a patient as the object 10,and receive an MR signal emitted from the patient. Specifically, the RFcoil 26 may transmit an RF signal, having the same frequency as that ofa precessional motion, to the patient toward an atomic nucleusperforming the precessional motion, stop the transmission of the RFsignal, and receive an MR signal emitted from the patient.

For example, in order to excite a specific atomic nucleus from a lowenergy level to a high energy level, the RF coil 26 may generate anelectromagnetic wave signal (for example, an RF signal) having an RFcorresponding to a kind of the specific atomic nucleus, and apply theelectromagnetic wave signal to the object 10. When the electromagneticwave signal generated by the RF coil 26 is applied to a specific atomicnucleus, the specific atomic nucleus may be excited from a low energylevel to a high energy level. Then, when the electromagnetic wave signalgenerated by the RF coil 26 dissipates, an energy level of the specificatomic nucleus to which the electromagnetic wave is applied may bechanged from the high energy level to the low energy level, and anelectromagnetic wave having a Larmor frequency may be emitted. The RFcoil 26 may receive an electromagnetic wave signal emitted from internalatomic nuclei of the object 10.

The RF coil 26 may be implemented as one RF transmission/reception coilthat has the function of generating an RF electromagnetic wave targetedat a kind of atomic nucleus and a function of receiving anelectromagnetic wave emitted from the atomic nucleus. Alternatively, theRF coil 26 may include a transmission RF coil that has the function ofgenerating an RF electromagnetic wave targeted at a kind of atomicnucleus and a reception RF coil that has a function of receiving anelectromagnetic wave emitted from the atomic nucleus.

Moreover, the RF coil 26 may be fixed to the gantry 20, or is detachablyattached to the gantry 20. The attachable/detachable RF coil 26 mayinclude a plurality of RF coils including a head RF coil, a chest RFcoil, a leg RF coil, a neck RF coil, a shoulder RF coil, a wrist RFcoil, and an ankle RF coil for some parts of an object, depending on adiagnosis part of the object.

Moreover, the RF coil 26 may communicate with an external device in awired/wireless manner, and perform dual tune communication based on acommunication frequency band.

Moreover, the RF coil 26 may include a birdcage coil, a surface coil,and a TEM coil, depending on a shape and structure of a coil.

Moreover, the RF coil 26 may include a transmission dedicated coil, areception dedicated coil, and a transmission/reception coil, dependingon an RF signal transmission/reception method.

Moreover, the RF coil 26 may include RF coils of various channels suchas 16 channels, 32 channels, 72 channels, and 144 channels.

Hereinafter, as an example, a case will be described in which the RFcoil 26 is an RF multi-coil that includes N number of coils respectivelycorresponding to a plurality of channels, namely, first to Nth channels.Here, the RF multi coil may be referred to as a multichannel RF coil.

The gantry 20 may further include a display 29 disposed outside thegantry 20 and a display (not shown) disposed inside the gantry 20. Auser may provide certain information to the object 10 by using thedisplays respectively disposed inside and outside the gantry 20.

The signal data acquirer 30 may control a gradient magnetic field whichis generated inside (i.e., the bore) the gantry 20, and controltransmission/reception of an RF signal and an MR signal, according to acertain MR sequence.

The signal data acquirer 30 may include a gradient amplifier 32, atransmission/reception switch 34, an RF transmission unit 36, i.e., RFtransmitter, and an RF data acquisition unit 38, i.e., RF receiver.

The gradient amplifier 32 may drive the gradient coil 24 included in thegantry 20, and supply a pulse signal, used to generate a gradientmagnetic field, to the gradient coil 24 according to control by agradient magnetic field control unit 54, i.e., a gradient magnetic fieldcontroller. Gradient magnetic fields in the X-axis, Y-axis, and Z-axisdirections may be synthesized by controlling the pulse signal suppliedfrom the gradient amplifier 32 to the gradient coil 24.

The RF transmission unit 36 and the RF data acquisition unit 38 maydrive the RF coil 26. The RF transmission unit 36 may supply an RF pulsehaving a Larmor frequency to the RF coil 26, and the RF data acquisitionunit 38 may receive an MR signal received by the RF coil 26.

The transmission/reception switch 34 may adjust a transmission/receptiondirection of each of the RF and MR signals. For example, in atransmission mode, the transmission/reception switch 34 may pass the RFsignal to be irradiated on the object 10 to the RF coil 26, and in areception mode, the transmission/reception switch 34 may pass the MRsignal received from the object 10 by means of the RF coil 26 to the RFdata acquisition unit 38. The transmission/reception switch 34 may becontrolled by a control signal from an RF control unit 56, i.e., an RFcontroller.

The monitor 40 may monitor or control the gantry 20 or elements includedin the gantry 20. The monitor 40 may include a system monitoring unit42, an object monitoring unit 44, a table control unit 46, i.e., a tablecontroller, and a display control unit 48, i.e., a display controller.

The system monitoring unit 42 may monitor or control a state of a staticmagnetic field, a state of a gradient magnetic field, a state of an RFsignal, a state of an RF coil, a state of a table, a state of an elementthat measures body information of an object, a power supply state, astate of a heat exchanger, a state of a compressor, etc.

The object monitoring unit 44 monitors a state of the object 10.Specifically, the object monitoring unit 44 may include a camera forobserving a movement or position of the object 10, a breathing analyzerfor analyzing the breathing by the object 10, an electrocardiogram (ECG)measurer for measuring an ECG of the object 10, or a body temperaturemeasurer for measuring a body temperature of the object 10.

The table control unit 46 controls a movement of the table 28 having theobject 10 located thereon. The table control unit 46 may control themovement of the table 28 according to sequence control by the systemcontroller 50. For example, in capturing a moving image of an object,the table control unit 46 may continuously or intermittently move thetable 28 according to the sequence control by the system controller 50,and thus photograph the object 10 at a view greater than a field of view(FOV) of the gantry 20.

The display control unit 48 controls the displays which are respectivelydisposed outside and inside the gantry 20. Specifically, the displaycontrol unit 48 may turn the displays disposed outside and inside thegantry 20 on or off, or control a screen displayed by each of thedisplays. Also, when a speaker is disposed inside or outside the gantry20, the display control unit 48 may turn the speaker or sound outputtedby the speaker on or off.

The system controller 50 may include a sequence control unit 52, i.e., asequence controller, that controls a processing of a sequence of signalsthat are generated in the gantry 20 or that controls a sequence ofsignals to be generated in the gantry 20. The system controller 50 mayfurther include a gantry control unit 58, i.e., a gantry controller,that controls the gantry 20 and elements mounted on the gantry 20.

The sequence control unit 52 may include the gradient magnetic fieldcontrol unit 54 that controls the gradient amplifier 32, and the RFcontrol unit 56 that controls the RF transmission unit 36, the RF dataacquisition unit 38, and the transmission/reception switch 34. Thesequence control unit 52 may control the gradient amplifier 32, the RFtransmission unit 36, the RF data acquisition unit 38, and thetransmission/reception switch 34 according to a pulse sequence receivedfrom the operating unit 60. Here, the pulse sequence includes allinformation necessary to control the gradient amplifier 32, the RFtransmission unit 36, the RF data acquisition unit 38, and thetransmission/reception switch 34, and for example, may includeinformation on an intensity of a pulse signal applied to the gradientcoil 24, an application time, and an application timing.

The operation unit 60 may provide pulse sequence information to thesystem controller 50, and simultaneously control an overall operation ofthe MRI apparatus.

The operating unit 60 may include an image processing unit 62, i.e., animage processor, that processes the MR signal received from the RF dataacquisition unit 38, an output unit 64, and an input unit 66.

The image processing unit 62 may process the MR signal received from theRF data acquisition unit 38 to generate an MRI image that is MRI imagedata of the object 10.

The image processing unit 62 may perform various signal processingoperations, such as amplification, frequency conversion, phasedetection, low-frequency amplification, and filtering, on the MR signalreceived by the RF data acquisition unit 38.

The image processing unit 62, for example, may arrange digital data in ak-space, and perform a 2D or 3D Fourier transform on the digital data toreconfigure the digital data into image data.

Moreover, depending on the case, the image processing unit 62 mayperform a synthesis processing or differential operation processing onthe image data. The synthesis processing may include an additionprocessing and a maximum intensity projection (MIP) processing on apixel. Also, the image processing unit 62 may store image data, on whichthe synthesis processing or differential operation processing has beenperformed, in addition to the reconfigured image data, in a memory (notshown) or an external server (not shown).

Moreover, the image processing unit 62 may parallelly perform varioussignal processings on the MR signal. For example, the image processingunit 62 may parallelly perform signal processing on a plurality of MRsignals received by a multichannel RF coil to reconfigure the pluralityof MR signals into image data.

The output unit 64 may output the image data generated or the image datareconfigured by the image processing unit 62 to a user. Also, the outputunit 64 may output information (which is necessary for the user tomanipulate the MRI apparatus) such as a user interface (UI), userinformation, or object information, in addition to the MRI image. Theoutput unit 64 may include a speaker, a printer, a CRT display, an LCD,a PDP, an OLED display, an FED, an LED display, a VFD, a DLP display, aPFD, a 3D display, a transparent display, etc., and include variousoutput devices within a scope obvious to those skilled in the art.

The user may input object information, parameter information, a scanningcondition, a pulse sequence, information on image synthesis ordifferential operation, etc. by using the input unit 66. The input unit66 may include a keyboard, a mouse, a trackball, a voice recognizer, agesture recognizer, a touch pad, a touch screen, etc., and includevarious input devices within a scope obvious to those skilled in theart.

FIG. 1 illustrates the signal data acquirer 30, the monitor 40, thesystem controller 50, and the operating unit 60 as separate elements.However, those skilled in the art understand that respective functionsperformed by the signal data acquirer 30, the monitor 40, the systemcontroller 50, and the operating unit 60 may be performed by differentelements. For example, it has been described above that the imageprocessing unit 62 converts the MR signal received by the RF dataacquisition unit 38 into a digital signal, but the conversion from theMR signal to the digital signal may be performed directly by the RF dataacquisition unit 38 or the RF coil 26.

The gantry 20, the signal data acquirer 30, the monitor 40, the systemcontroller 50, and the operating unit 60 may be connected to each otherin a wired/wireless manner. When they are connected in a wired manner,an element for synchronizing a clock therebetween may be furtherprovided. Communication between the gantry 20, the signal data acquirer30, the monitor 40, the system controller 50, and the operating unit 60may use a high-speed digital interface, such as low-voltage differentialsignaling (LVDS), asynchronous serial communication such as a universalasynchronous receiver transmitter (UART), a low-delay network protocolsuch as synchronous serial communication or a can area network (CAN), oroptical communication, and use various communication schemes within ascope obvious to those skilled in the art.

FIG. 2 is a diagram illustrating an MRI apparatus 200 according to anembodiment of the present invention.

Referring to FIG. 2, the MRI apparatus 200 according to an embodiment ofthe present invention includes a data acquirer 210 and an imageprocessor 230. Also, the MRI apparatus 200 may be connected, by awired/wireless manner, to an RF multi-coil 205 included in a gantry, andthe data acquirer 210 may receive an MR signal sensed by the RFmulti-coil 205. In FIG. 2, the RF multi-coil 205 corresponds to the RFcoil 26 of FIG. 1. Also, the data acquirer 210 may be connected to theRF data acquisition unit 38 of FIG. 1, and may receive an MR signal fromthe RF data acquisition unit 38.

The MRI apparatus 200 is an apparatus that performs imaging on an MRimage by using an MR signal acquired by a plurality of channel coilsincluded in the RF multi-coil 205.

The RF multi-coil 205 includes the plurality of channel coils. Indetail, the RF multi-coil 205 includes first to nth channel coils, andeach of the n channel coils receives an MR signal that is an RF signal.

In detail, the RF multi-coil 205 applies an RF signal to an object toexcite nuclear spins of the object. Then, the nuclear spins of theobject are transit to a high energy state by the applied RF signal, andsubsequently returns to the original energy state to emit the remainingenergy to the outside. At this time, the energy emitted from the nuclearspins is an MR signal that is an RF signal, and the RF multi-coil 205may sense the emitted MR signal to transmit the sensed MR signal to thedata acquirer 210.

That is, the data acquirer 210 receives the MR signal acquired by the RFmulti-coil 205 including the plurality of channel coils.

For example, the data acquirer 210 arranges MR signals, which arerespectively received from the n channel coils included in the RFmulti-coil 205, in a K-space to generate n pieces of raw data. Indetail, the raw data may be a signal that is generated by arranging theMR signals, which are RF signals respectively received from the coils bychannel included in the RF multi-coil 205, in the K-space, and may beunder-sampled K-space data. Here, the K-space is a spatial frequencydomain, and is formed by a Kx axis corresponding to frequency encodingand a Ky axis corresponding to phase encoding.

Moreover, the data acquirer 210 may transfer the MR signals, receivedfrom the RF multi-coil 205, to the image processor 230. In this case,the image processor 230 may arrange the MR signals, transferred from thedata acquirer 210, in the K-space to generate the under-sampled K-spacedata. In detail, the data acquirer 210 may perform under-sampling on theMR signals respectively corresponding to the plurality of channel coilsincluded in the RF multi-coil 205 to generate a plurality of pieces ofunder-sampled K-space data respectively corresponding to the pluralityof channel coils.

For example, when the RF multi-coil 205 includes the n channel coils,the data acquirer 210 may receive n number of MR signal setscorresponding to n number of channel coils to generate n pieces ofunder-sampled K-space data.

The following description will be made on an operation in which the dataacquirer 210 receives the MR signals from the RF multi-coil 205, andperforms under-sampling on the MR signals to generate the under-sampledK-space data.

The data acquirer 210 performs under-sampling on the MR signals,respectively received from the plurality of channel coils, atnon-uniform intervals to acquire the under-sampled K-space datarespectively corresponding to the plurality of channel coils. Here, theunder-sampled K-space data include a plurality of pieces of acquiredline data. That is, the data acquirer 210 may perform under-sampling onthe MR signals, respectively received from the plurality of channelcoils, at non-uniform intervals to acquire a plurality of pieces of linedata. Also, the under-sampled K-space data is image data which isincomplete for imaging MR images by channel.

The image processor 230 restores a plurality of pieces of K-space datarespectively corresponding to the plurality of channel coils by using arelationship between the acquired plurality of pieces of line data.Also, the image processor 230 may generate a final MR image by using therestored plurality of pieces of K-space data.

Detailed operations of the data acquirer 210 and the image processor 230will be described below in detail with reference to FIGS. 3 to 5.

Moreover, the MRI apparatus 200 may be connected to a display unit 250by a wired/wireless manner. The display unit 250 may be included in theoutput unit 64 of FIG. 1, or may be provided separately. Also, thedisplay unit 250 may be included in the MRI apparatus 200. The displayunit 250 may display the final MR image generated by the image processor230 in order for a user to visually recognize the final MR image.

FIGS. 3A and 3B are diagrams for describing an operation of the MRIapparatus 200 according to an embodiment of the present invention.

The data acquirer 210 performs under-sampling on the MR signals,respectively received from the plurality of channel coils, atnon-uniform intervals to generate the plurality of pieces ofunder-sampled K-space data respectively corresponding to the pluralityof channel coils. In detail, the data acquirer 210 wholly performsunder-sampling in a K-space corresponding to each of a plurality ofchannel coils to generate under-sampled K-space data. In detail, sincethe under-sampling is wholly performed in the K-space, sampling isperformed in a high frequency domain as well as a low frequency domainwhich is a central region of the K-space. Therefore, since theunder-sampling is wholly performed in the K-space, the MRI apparatus 200has a merit in restoring an image in the low frequency domain and thehigh frequency domain which correspond to an entire region of theK-space.

In FIGS. 3A and 3B, under-sampled K-space data that is generated byperforming sampling of an MR signal received from one channel coil isillustrated as an example.

In detail, FIG. 3A illustrates an example of under-sampled K-space data310. FIG. 3B illustrates another example of under-sampled K-space data.

Referring to FIG. 3A, the data acquirer 210 performs under-sampling of areceived MR signal at non-uniform intervals. That is, in under-samplingfor acquiring line data less than the total number of data lines ofK-space data, intervals of acquired line data by under-sampling arenon-uniform. For example, in acquiring K-space data having a resolutionof 256*256, the data acquirer 210 does not perform sampling on all 256lines but performs sampling on only some pieces of line data. Here, inthe K-space data having a resolution of 256*256, the number of lines maybe 256 lines which are disposed in a Ky direction. Furthermore, the dataacquirer 210 performs sampling at non-uniform intervals of acquiredlines. In FIG. 3A, data acquired through sampling is illustrated ascircles, and unsampled data is illustrated as dot lines.

In detail, in the K-space data 310, a line acquired through sampling maybe determined based on at least one selected from the number “b” ofblocks included in one piece of K-space data 310 corresponding to onechannel coil, a block size “N”, and an acceleration factor (AF). Here,the block size denotes the number of data lines included in one block(for example, in a block 330) included in the K-space data 310. Thenumber of blocks denotes an order of a block which is disposed in theK-space data 310. For example, in K-space data in which a size of afield of view (FOV) is 256*256, when the number of lines included in oneblock 330 is 9, the number of blocks included in the K-space data 310 is29 (256/9=28.44). Also, in FIG. 3, in the K-space data 310, the numberof blocks in a first-disposed block 330 is 1 and the number of blocks ina second-disposed block 350 is 2.

For example, a line acquired through sampling may be determined based onthe following Equation (1).

First acquisition line index: 1+N*(b−1)

Second acquisition line index: 1+AF+N*(b−1)

Third acquisition line index: 1+AF*2+N*(b−1)

Fourth acquisition line index: 2+AF*2+N*(b−1)

Fifth acquisition line index: 2+AF*3+N*(b−1)  [Equation 1]

For example, a first block 330 included in the K-space data 310 will bedescribed as an example. In this case, an AF value is 2, a block size“N” is 9, and the number “b” of blocks is 1. Therefore, when an AF “2”,a block size “9” and the number “1” of blocks are substituted intoEquation (1),

First acquisition line index: 1+9*(1−1)=1,

Second acquisition line index: 1+2+9*(1−1)=3,

Third acquisition line index: 1+2*2+9*(1−1)=5,

Fourth acquisition line index: 2+2*2+9*(1−1)=6,

Fifth acquisition line index: 2+2*3+9*(1−1)=8

Therefore, referring to FIG. 3A, when the K-space data has 256 lineswith respect to a Ky axis, in the first block 330, the data acquirer 210may acquire signal values of 1, 3, 5, 6, and 8 lines, and does notacquire signal values of 2, 4, 7, and 9 lines. In detail, in performingunder-sampling by the data acquirer 210, a sampling interval of 1, 3,and 5 lines is a two-line interval, a sampling interval of 5 and 6 linesis a one-line interval, and a sampling interval of 6, 8, 10, 12, and 14lines is a two-line interval. Therefore, under-sampling intervals arenon-uniform.

Moreover, the data acquirer 210 may divide a K-space corresponding toone channel coil into a plurality of blocks, and perform under-samplingon the divided plurality of blocks at non-uniform intervals to acquire aplurality of pieces of line data.

Referring to FIG. 3, in acquiring K-space data corresponding to onechannel coil, the data acquirer 210 may divide a K-space correspondingto one channel coil into a plurality of blocks, for example, first andsecond blocks 330 and 350, and may simultaneously perform sampling onthe plurality of blocks, namely, the first and second blocks 330 and350.

For example, as illustrated in FIG. 3, when one block includes ninelines, in acquiring K-space data including 256 lines, the data acquirer210 may divide a K-space corresponding to the one channel coil into 29blocks, and perform under-sampling for each of the blocks to acquire aplurality of pieces of line data. In detail, in acquiring K-space dataincluding 256 lines, when a K-space is divided into 29 blocks, one blockmay include 9 pieces of line data, four (256−9*28=4) pieces of line datamay remain in a 29th block which is a last block, and the data acquirer210 may perform sampling on all of the remaining four pieces of linedata.

Moreover, a plurality of divided blocks may have a non-uniformunder-sampling pattern which is the same under-sampling intervalpattern. And, the same under-sampling interval pattern is non-uniforminterval pattern. In FIG. 3A, a case in which a plurality of blocks havethe same pattern is illustrated as an example. In detail, since thefirst block 330 and the second block 350 have the same sampling intervalpattern, pieces of data are respectively acquired from first, third,fifth, sixth, and eighth lines of each of the first and second blocks330 and 350, and pieces of data are not acquired from second, fourth,seventh, and ninth lines of each of the first and second blocks 330 and350. In FIG. 3A, a line from which data is not acquired is illustratedas a dashed line.

Moreover, the data acquirer 210 performs under-sampling at non-uniformintervals in a whole K-space corresponding to each of a plurality ofchannel coils included in the RF multi-coil 205. That is, the dataacquirer 210 wholly performs under-sampling to acquire pieces of linedata.

Moreover, the same non-uniform under-sampling pattern may be applied toa plurality of blocks included in the K-space data 310, or differentnon-uniform under-sampling patterns may be respectively applied to theblocks included in the K-space data 310. Also, different non-uniformunder-sampling patterns may be respectively applied to at least oneblock included in the blocks included in the K-space data 310.

The image processor 230 may restore a plurality of pieces of unacquiredline data by using a relationship between a plurality of pieces ofacquired line data, and restore K-space data corresponding to a certainchannel coil by using at least one of the restored plurality of piecesof line data and the plurality of pieces of acquired line data.

In an example illustrated in FIG. 3A, the image processor 230 mayacquire data from the first, third, fifth, sixth, and eighth lines ofeach of the first and second blocks 330 and 350, and restore a pluralityof pieces of unacquired line data by using a relationship based on aspatial distance between the acquired plurality of pieces of line data.In detail, the image processor 230 may restore a plurality of pieces ofunacquired line data by using a relationship based on a spatial distancebetween at least two of the acquired plurality of pieces of line data.

In detail, the image processor 230 may set one of the acquired pluralityof pieces of line data to reference line data, and when spatialdistances between the reference line data and at least two of theacquired plurality of pieces of line data have a first relationship, theimage processor 230 may restore the plurality of pieces of unacquiredline data by using the first relationship.

Referring to FIG. 3A, the image processor 230 may set sixth line data inthe acquired plurality of pieces of line data in the first block 330, asthe reference line data. Referring to a portion 321 of FIG. 3, spatialdistances between the sixth line data (which is the reference line data)and first, third, fifth, and eighth line data (which are the otheracquired line data) have a five-line interval, a three-line interval, aone-line interval, and a two-line interval, respectively. The imageprocessor 230 may define a relationship between the reference line dataand the acquired plurality of pieces of line data as an intervalrelationship “5, 3, 1, and 2”. Here, one of the acquired plurality ofpieces of line data may be set to the reference line data.

Moreover, the number and sizes of blocks included in one piece ofK-space data (for example, 310 of FIG. 3A) may be changed based on atleast one selected from a hardware type of the RF multi-coil 205 and apart of an object to be photographed. In detail, a size and a type ofthe RF multi-coil 205 may correspond to the hardware type of the RFmulti-coil 205. Also, the part of the object to be photographed may beclassified based on body parts such as a head, a neck, a stomach, aback, and an ankle. For example, the RF multi-coil 205 used tophotograph a head may have a dome shape. As another example, the RFmulti-coil 205 used to photograph a stomach or a leg may have acylindrical shape. As another example, the RF multi-coil 205 used tophotograph a back may have a plate shape.

In detail, the number and sizes of blocks included in the K-space data310 may be changed based on a body part such as a head, a neck, astomach, a back, or an ankle photographed by the RF multi-coil 205 or ashape of the RF multi-coil 205 such as a dome shape, a cylindricalshape, or a plate shape. Also, the number of blocks and a size of eachblock included in the K-space data 310 may be set to values which areexperimentally optimized.

Moreover, a shape of a non-uniform under-sampling pattern in a blockincluded in the one piece of K-space data 310 may be changed based on atleast one selected from a hardware type of the RF multi-coil 205 and apart of an object to be photographed. In detail, the shape of thenon-uniform under-sampling pattern may be changed based on a body partsuch as a head, a neck, a stomach, a back, or an ankle photographed bythe RF multi-coil 205 or a shape of the RF multi-coil 205 such as a domeshape, a cylindrical shape, or a plate shape. Also, the shape of thenon-uniform under-sampling pattern may be set to a value which isexperimentally optimized.

Moreover, the number and sizes of blocks included in one piece ofK-space data (for example, 310 of FIG. 3A) may be set by a user. Also, ashape of a non-uniform under-sampling pattern in a block included in theone piece of K-space data 310 may be set by the user.

Moreover, the image processor 230 may calculate a spatial correlationcoefficient corresponding to the first relationship by using thereference line data and at least two of the acquired plurality of piecesof line data. Furthermore, the image processor 230 may restore theplurality of pieces of unacquired line data by using the spatialcorrelation coefficient.

Calculating the spatial correlation coefficient and restoring theunacquired line data will now be described in detail with reference toFIG. 4A.

FIG. 4A is a diagram for describing an operation of the MRI apparatusaccording to an embodiment of the present invention. FIG. 4A (a) is adiagram for describing calculation of a spatial correlation coefficient.FIG. 4A (b) is a diagram for describing estimation of unacquired linedata.

Referring to FIG. 4A (a), in a matrix operation, a left term 410 iscomposed of signal values included in a plurality of pieces of acquiredline data, and a right term 420 is composed of signal values included inreference line data. Kc denotes a spatial correlation coefficient.

In detail, the spatial correlation coefficient is a spatial interactionvalue between signal values measured adjacent to a certain value. Atarget signal value to estimate may be calculated by performing thematrix operation on adjacent signals and the spatial correlationcoefficient.

Referring to FIG. 4A (a), the left term 410 is composed of signal valuesincluded in the first line data, third line data, fifth line data, andeighth line data that are signal values measured from the first block330, and the right term 420 is composed of signal values included in thesixth line data (the reference line data) included in the first block330. Therefore, since the left term 410 and the right term 420 includesignal values of a plurality of pieces of acquired line data, thespatial correlation coefficient Kc may be acquired through an inversematrix operation illustrated in FIG. 4A (a).

In detail, in order to acquire the spatial correlation coefficient Kc,as described above, the data acquirer 210 acquires K-space data (forexample, 310 of FIG. 3A) by coil according to a non-uniformunder-sampling pattern. An inverse operation of an arithmetic operationillustrated in FIG. 4A (a) is performed by using acquired line data.Therefore, the spatial correlation coefficient Kc is acquired byperforming the inverse operation.

The image processor 230 may acquire unacquired line data by using thespatial correlation coefficient Kc. In detail, when the spatialcorrelation coefficient Kc is acquired, the image processor 230 mayperform a matrix multiplication operation on the spatial correlationcoefficient Kc and signal values of line data having the firstrelationship with unacquired line data to estimate the unacquired linedata. An operation of restoring the unacquired line data by using thespatial correlation coefficient Kc may be performed for each block asdescribed above with reference to FIGS. 3A and 3B.

Referring to FIG. 4A (b), in a matrix operation, a left term 430 iscomposed of signal values included in a plurality of pieces of line datahaving the first relationship with unacquired line data, and a rightterm 440 is composed of signal values included in the unacquired linedata to estimate. Kc denotes a spatial correlation coefficient. Here, Kcmay be referred to as a weighting matrix “W”.

Moreover, Kc and the inverse matrix operation described above withreference to FIG. 4A (a) and (b) may be acquired or performed by variousmethods. In detail, Kc and the inverse matrix operation are disclosed inthe paper “Introduction to inverse problems in imaging” presented byMario Bertero & Patrizia Boccacci and the paper “Inverse problems theoryand methods for model parameter estimation” presented by AlbertTarantola, and thus, their detailed descriptions are not provided.

Hereinafter, the weighting matrix “W” will be described in detail withreference to FIG. 4B.

As described above, when Kc is referred to as the weighting matrix “W”,the weighting matrix “W” is determined by a block group “g”, a coilnumber “j”, an acceleration factor “r”, the number of coils “Nc”, andetc.

Referring to FIG. 4B (a), an equation for calculating the spatialcorrelation coefficient Kc described above with reference to FIG. 4A (a)is illustrated. In detail, the equation illustrated in FIG. 4B (a) is anexample of an equation which is usable for calculating the spatialcorrelation coefficient Kc.

Referring to an equation illustrated in FIG. 4B (a), a left term 450, aright term 470, and a weighting matrix 460 respectively correspond to aright term 420 of a matrix operation illustrated in FIG. 4A (a), a leftterm 410 of the matrix operation, and the spatial correlationcoefficient Kc.

FIG. 4B (b) is a diagram for describing factors which are used in theequation illustrated in FIG. 4B (a).

Referring to FIG. 4B (b), g denotes a block group. When K-space data(for example, 310 of FIG. 3A) corresponding to one channel coil isdivided into a plurality of blocks, a block (for example, 330) may be ablock group. When the K-space data 310 has a 256*256 size and one blockis nine lines as illustrated in FIG. 3A, the number of blocks is 29, andthus, g may have a value of 1 to 29. j denotes a coil number, and whenthe RF multi-coil 205 includes a plurality of coils, j denotes a numberof a coil included in the RF multi-coil 205. B denotes a block size. Indetail, in FIG. 3A a block size may have a value “9”. n denotes a blocknumber in a group, and denotes a block number of a certain blockincluded in the one piece of K-space data 310 corresponding to one coil.In detail, a block number of a block 330 which is first arranged in theK-space data 310 may be 1, and a block number of a second-arranged block350 may be 2. Nc denotes the number of coils included in the RFmulti-coil 205. Nb denotes the number of blocks adjacent to a currentblock. In detail, the number of blocks which are disposed adjacent to acurrent block (for example, 350) in the K-space data 310 may be 28 whichis the number of blocks other than the current block 350 in the K-spacedata. N_(r) denotes the number of data which are disposed at the rightof a selected point in frequency encoding data which is disposed in a Kxdirection in a K-space, and N_(l) denotes the number of data which aredisposed at the left of the selected point. r denotes an accelerationfactor. M(b,r) denotes a non-uniform under-sampling pattern in which anacceleration factor in a b block has an r value.

In detail, Sg,j denotes a signal value at one selected point in acertain block in K-space data (for example, 310 of FIG. 3A), and Sg,cdenotes signal values which are acquired from the other point in thecertain block. Wg,j,r denotes a weighting matrix applied to a block anddenotes the above-described spatial correlation coefficient Kc.

FIG. 4B (c) is a diagram illustrating a weighting matrix.

A weighting matrix 490 may be acquired by performing an inverseoperation of the equation described above with reference to FIG. 4B (a).In detail, the weighting matrix 460 which is calculated by performingthe inverse operation of the equation described above with reference toFIG. 4B (a) is the weighting matrix 490 described above with referenceto FIG. 4B (c).

Referring to a portion 323 of FIG. 3A, when the spatial correlationcoefficient Kc is acquired, the image processor 230 may substitutesignal values (which are included in 255 line data 360, first line data,third line data, and sixth line data that are a plurality of pieces ofline data having the first relationship (i.e., the interval relationship“5, 3, 1, and 2” with respect to the unacquired line data of a fourthline to estimate) with the unacquired line data of the fourth line) intothe left term 430 of the matrix operation, and multiply the left term430 and the spatial correlation coefficient Kc to calculate values ofthe right term 440 which are signal values of the unacquired line dataof the fourth line. The image processor 230 may restore the unacquiredline data to the calculated values of the right term 440. Here, amultiplication operation may be a multiplication operation betweenmatrixes described above with reference to FIG. 4A.

Unacquired line data may be restored for each block.

In detail, data may be simultaneously restored for a plurality ofblocks. For example, the image processor 230 may calculate a spatialcorrelation coefficient applied to an operation of restoring data of thefirst block 330, and calculate a spatial correlation coefficient appliedto an operation of restoring data of the second block 350. Furthermore,while unacquired line data of a fourth line of the first block 330 isbeing restored, unacquired line data of a thirteenth line that is afourth line of the second block 350 may be restored.

Moreover, data may be separately restored for each of the plurality ofblocks. For example, the image processor 230 may calculate the spatialcorrelation coefficient, applied to the operation of restoring data ofthe first block 330, to restore the data of the first block 330, andthen may calculate the spatial correlation coefficient, applied to theoperation of restoring data of the second block 350, to restore the dataof the second block 350.

Moreover, the image processor 230 may estimate a plurality of pieces ofunacquired line data by using the restored line data as acquired linedata.

To describe the operation of restoring data of the second block 350 asan example, when unacquired line data of the thirteenth line is restoredby using a plurality of pieces of acquired line data as in a portion363, the image processor 230 may restore 11-line data that is secondline data of the second block 350, by using 6-line data, 8-line data,and 10-line data that are the plurality of pieces of acquired line dataand the restored 13-line data.

That is, referring to a portion 365, 11-line data may be restored byusing 6-line data, 8-line data, 10-line data, and 13-line data that area plurality of pieces of line data having the first relationship withthe 11-line data to restore.

In detail, the image processor 230 may substitute signal values whichare included in the 6-line data, 8-line data, 10-line data, and 13-linedata illustrated in the portion 365 into the left term 430, and multiplythe left term 430 and the spatial correlation coefficient Kc tocalculate signal values included in the 11-line data. Also, the imageprocessor 230 may restore the 11-line data by using the calculatedsignal values. Furthermore, the restored 11-line data may besubsequently used to restore 18-line data.

Moreover, the image processor 230 may sequentially restore a pluralityof pieces of unacquired line data, included in each block, in apredetermined order. In detail, the image processor 230 starts to firstrestore unacquired line data which includes all signal values of aplurality of pieces of line data with the first relationship with theunacquired line data to restore.

In an example illustrated in FIG. 3A, restoration for a first block maybe performed in the order of 4-line data, 2-line data, 9-line data, and7-line data.

In the example illustrated in FIG. 3A, when K-space data correspondingto one channel coil includes 256 lines and one block includes ninepieces of line data, in parallelly restoring unacquired line data byblock, the image processor 230 may calculate a spatial correlationcoefficient which is used to restore the unacquired line data of eachblock, and then restore the unacquired line data in the following order:

First-restored unacquired line data: 4, 13, 22, 31, . . . , and 256

Second-restored unacquired line data: 2, 11, 20, 29, . . . , and 254

Third-restored unacquired line data: 9, 18, 27, 36, . . . , and 252

Third-restored unacquired line data: 7, 16, 25, 34, . . . , and 250

In detail, referring to FIG. 3A, in a first block 330, unacquired linedata may be restored in the order of 4, 2, 9, and 7 lines.

The image processor 230, as described above, may estimate all unacquiredline data by using a relationship between pieces of line data which areacquired through sampling performed at non-uniform intervals. Therefore,the image processor 230 may acquire restored pieces of K-space datawhich are full-sampled K-space data by channel coil.

Moreover, as described above, lines acquired from one block may bedetermined based on at least one selected from the number “b” of blocksincluded in the K-space data 310, a block size “b”, and an AF.Therefore, acquired line data or a non-uniform sampling pattern may beadjusted by adjusting at least one selected from the number “b” ofblocks included in the K-space data 310, the block size “b”, and the AF.

As another example, it is assumed that an AF value is 3, a block size“N” is 13, and the number “b” of blocks is 1. Therefore, when an AF “3”,a block size “13” and the number “1” of blocks are substituted intoEquation (1),

First acquisition line index: 1+13*(1−1)=1,

Second acquisition line index: 1+3+13*(1−1)=4,

Third acquisition line index: 1+3*2+13*(1−1)=7,

Fourth acquisition line index: 2+3*2+13*(1−1)=8,

Fifth acquisition line index: 2+3*3+13*(1−1)=11

Therefore, referring to FIG. 3B, when K-space data has 256 lines on a Kyaxis, in a first block 370, signal values of 1, 4, 7, 8, and 11 linesare acquired, and signal values of 2, 3, 5, 6, 9, and 11 lines are notacquired. In detail, in performing under-sampling by the data acquirer210, a sampling interval of 1, 4, and 7 lines is a 3-line interval, asampling interval of 7 and 8 lines is a 1-line interval, and a samplinginterval of 11, 14, 17, and 20 lines is a 3-line interval, whereby anunder-sampling interval is non-uniform.

In detail, referring to FIG. 3B, since the first block 370 and a secondblock 380 have the same sampling interval pattern, data is acquired infirst, fourth, seventh, eighth, and eleventh lines in a block, and datais not acquired in second, third, fifth, sixth, ninth, and tenth linesin the block. In FIG. 3B, a line in which data is not acquired isillustrated as a dot line.

Referring to FIG. 3B, in the same method as that of FIG. 3A, the imageprocessor 230 may set, as reference line data, eighth line data amongpieces of line data which are acquired in the first block 370. Referringto a portion 371 of FIG. 3B, spatial distances between the eighth linedata (which is the reference line data) and first, fourth, seventh, andeleventh line data (which are other acquired pieces of line data) have a7-line interval, a 4-line interval, a 1-line interval, and a 3-lineinterval. The image processor 230 may define a relationship between thereference line data and the acquired pieces of line data as an intervalrelationship (7, 4, 1, 3). Here, one of the acquired pieces of line datamay be set as the reference line data.

Moreover, in the same method as that of FIG. 3A, the image processor 230may restore unacquired line data.

FIG. 5 is another diagram for describing an operation of the MRIapparatus according to an embodiment of the present invention.

Referring to FIG. 5, when the RF multi-coil 205 includes n number ofchannel coils COIL1 to COILN, the image processor 230 may restoreunacquired line data from n pieces of under-sampled K-space data 510through 520 respectively corresponding to the n channel coils COIL1through COILN. Therefore, the image processor 230 may acquire n piecesof restored K-space data 515 through 525 respectively corresponding tothe n channel coils COIL1 through COILN.

Moreover, the image processor 230 may perform a spatial transform on theplurality of pieces of restored K-space data 515 through 525respectively corresponding to the plurality of channel coils COIL1through COILN to generate a plurality of MR images 517 through 527 bychannel, and acquire a final MR image 550 by using the plurality of MRimages 517 through 527 by channel.

In detail, the image processor 230 may perform an inverse Fouriertransform (IFT) or an inverse fast Fourier transform (IFFT) on therestored K-space data 515 through 525, for transforming the restoredK-space data 515 and 525 from a frequency domain into a spatial domain.The image processor 230 may calculate a sum of squares or complex sum ofn number of inverse fast Fourier-transformed MR images 517 through 527to acquire the final MR image 550.

Moreover, the image processor 230 may divide a plurality of blocks intoa certain number of groups, and set different non-uniform under-samplinginterval patterns for the divided groups.

Moreover, the image processor 230 may cause a plurality of blocks, intowhich a K-space corresponding to one channel coil is divided, to havedifferent non-uniform under-sampling interval patterns.

For example, the image processor 230 may divide K-space data into aplurality of blocks, distinguish at least one first block and at leastone second block, and set different under-sampling interval patterns inorder for the first and second blocks to have the differentunder-sampling interval patterns. In detail, the first block may be setto have an under-sampling interval pattern that causes the first blockto include acquired line data more than or less than those of the secondblock.

As a detailed example, when the K-space is divided into 29 blocks inorder for one block to include nine lines, as in the example illustratedin FIG. 3A, the image processor 230 may more densely set under-samplinginterval patterns of fourteenth and fifteenth blocks, disposed at acenter portion of the K-space, than those of the other blocks.

As another example, the image processor 230 may divide the K-space intoa plurality of blocks, distinguish at least one first block, at leastone second block, and at least one third block, and set differentunder-sampling interval patterns in order for the first to third blocksto have the different under-sampling interval patterns. As a detailedexample, the second block may be set to have an under-sampling intervalpattern that causes the second block to include acquired line data lessthan those of the first block, and the third block may be set to have anunder-sampling interval pattern that causes the third block to includeacquired line data more than those of the first block. In this case, thethird block may be a block that is disposed more adjacent to a centerline of the K-space than the first or second block. And, the secondblock may be a block that is disposed at an edge portion of the K-spaceas compared to the first block.

For example, when the K-space is divided into twenty-eight blocks inorder for one block to include nine lines, as in the example illustratedin FIG. 3A, the image processor 230 may more densely set under-samplinginterval patterns of the fourteenth and fifteenth blocks, disposed atthe center portion of the K-space, than those of third to thirteenthblocks and sixteenth to twenty-sixth blocks that are the other blocks.Furthermore, the image processor 230 may less densely set under-samplinginterval patterns of first and second blocks and twenty-seventh andtwenty-eighth blocks, disposed at an edge portion of the K-space, thanthose of the third to thirteenth blocks and the sixteenth totwenty-sixth blocks that are the other blocks.

Moreover, the image processor 230 may divide the K-space into aplurality of blocks, and may more densely set an under-sampling intervalpattern of a block which is closer to the center line of the K-space.For example, when the K-space includes 256 pieces of line data, thecenter line is a 128th line. In this case, the image processor 230 maymore densely set an under-sampling interval pattern of a block adjacentto the 128th line than those of the other blocks.

A center portion of the K-space is a low-frequency domain. When thenumber of sampled line data increases by densely setting anunder-sampling interval pattern of a block included in the low-frequencydomain of the K-space, a clear MR image is acquired, thus improving aquality of a final MR image.

Moreover, a peripheral portion of the K-space is a high-frequencydomain. When the number of sampled line data decreases by less denselysetting an under-sampling interval pattern of a block included in thehigh-frequency domain of the K-space, an acquisition time of an MR imageis shortened without a large degradation in a quality of the MR image.

As described above, the data acquirer 210 under-samples MR signals,which are respectively received from the plurality of channel coilsincluded in the RF multi-coil 205, at non-uniform intervals in a wholeK-space to acquire pieces of line data. In detail, referring to FIG. 3,the pieces of line data are acquired according to a non-uniformunder-sampling pattern in the K-space data 310.

The image processor 230 restores complete K-space data corresponding toeach of the plurality of channel coils by using a relationship betweenthe pieces of line data which are acquired by performing under-samplingat non-uniform intervals. In detail, referring to FIGS. 3 to 5, theimage processor 230 may acquire the spatial correlation coefficient Kcby using a relationship, based on a spatial distance between theacquired pieces of line data, between the acquired pieces of line data,and restore complete K-space data (for example, 515 and 525)corresponding to each channel coil by using the acquired spatialcorrelation coefficient Kc.

FIG. 6 is a flowchart of an MR imaging method 600 according to anembodiment of the present invention. The method 600 of imaging an MRimage according to an embodiment of the present invention can beperformed by the MRI apparatus 200 according to an embodiment of thepresent invention described above with reference to FIGS. 1 to 5, andthus, the same descriptions provided with regard to FIG. 1 are notrepeated.

Referring to FIG. 6, in operation 610, the method 600 of imaging an MRimage according to an embodiment of the present invention performsunder-sampling on MR signals, respectively received from the pluralityof channel coils included in the RF multi-coil 205, at non-uniformintervals to acquire a plurality of pieces of line data. Operation 610may be performed by the data acquirer 210.

In operation 620, the method 600 restores a plurality of pieces ofK-space data respectively corresponding to the plurality of channelcoils by using a relationship between the plurality of pieces of linedata which are acquired in operation 610. Operation 620 may be performedby the image processor 230 of the MRI apparatus 200.

Moreover, an MR image imaging method 600 may be performed as follows.

In detail, in operation 610, the data acquirer 210 under-samples MRsignals, which are respectively received from the plurality of channelcoils included in the RF multi-coil 205, at non-uniform intervals in awhole K-space to acquire pieces of line data. In detail, referring toFIG. 3, the pieces of line data are acquired according to a non-uniformunder-sampling pattern in the K-space data 310.

Moreover, in operation 620, the image processor 230 restores completeK-space data corresponding to each of the plurality of channel coils byusing a relationship between the pieces of line data which are acquiredby performing under-sampling at non-uniform intervals. In detail,referring to FIGS. 3 to 5, the image processor 230 may acquire thespatial correlation coefficient Kc by using a relationship, based on aspatial distance between the acquired pieces of line data, between theacquired pieces of line data, and restore complete K-space data (forexample, 515 and 525) corresponding to each channel coil by using theacquired spatial correlation coefficient Kc.

FIG. 7 is a flowchart of an MR imaging method 700 according to anotherembodiment of the present invention. The method 700 of imaging an MRimage according to another embodiment of the present can be performed bythe MRI apparatus 200 according to an embodiment of the presentinvention described above with reference to FIGS. 1 to 5. Also,operations 710 and 720 of FIG. 7 respectively correspond to operations610 and 620 of FIG. 6. Thus, the same descriptions provided with regardto FIG. 1 are not repeated.

Referring to FIG. 7, the method 700 divides a K-space, corresponding toeach of the plurality of channel coils included in the RF multi-coil205, into a plurality of blocks, and performs under-sampling on MRsignals, respectively received from the plurality of blocks which eachinclude a plurality of channel coils, at non-uniform intervals toacquire a plurality of pieces of line data in operation 710. Operation710 may be performed by the data acquirer 210.

Here, as illustrated in FIG. 3A, the plurality of blocks may have thesame under-sampling interval pattern which is non-uniform. Also, theplurality of blocks may be divided into a certain number of groups,which may be set to have different under-sampling interval patterns.Also, the plurality of blocks may have different under-sampling intervalpatterns.

In operation 720, the method 700 restores a plurality of pieces ofK-space data respectively corresponding to the plurality of channelcoils by using a relationship between the plurality of pieces of linedata which are acquired in operation 710. Operation 720 may be performedby the image processor 230.

In detail, the method 700 may restore a plurality of pieces ofunacquired line data by using the relationship between the plurality ofpieces of line data which are acquired in operation 710, and restoreK-space data corresponding to a channel coil by using at least one ofthe restored plurality of pieces of line data and the acquired pluralityof pieces of line data.

In detail, in operation 720, the method 700 sets one of the plurality ofpieces of line data (which are acquired in operation 710) to referenceline data. When spatial distances between the reference line data and atleast two of the acquired plurality of pieces of line data have a firstrelationship, the method 700 may restore the plurality of pieces ofunacquired line data by using the first relationship.

In operation 720, restoring the unacquired line data has been describedabove in detail with reference to FIGS. 3A and 4A, and thus, the samedescriptions provided with regard to FIGS. 3A and 4A are not repeated.

In operation 730, the method 700 restores K-space data corresponding toa channel coil by using at least one of the plurality of pieces ofunacquired line data (which are restored in operation 720) and theplurality of pieces of line data which are acquired in operation 710.Operation 730 may be performed by the image processor 230.

Subsequently, in operation 740, the method 700 performs a spatialtransform on the restored plurality of pieces of K-space datarespectively corresponding to a plurality of channel coils to generate aplurality of MR images by channel. Operation 740 may be performed by theimage processor 230. In detail, the method 700 may perform an inverseFourier transform on the plurality of pieces of K-space datarespectively corresponding to the plurality of channel coils to generatethe plurality of MR images by channel.

In operation 750, the method 700 acquires a final MR image by using theplurality of MR images by channel which are acquired in operation 740.Operation 740 may be performed by the image processor 230. In detail,the method 700 may calculate a sum of squares or complex sum of theplurality of MR images by channel to generate the final MR image.

FIG. 8 is a diagram for describing an MR image generated by the MRIapparatus and the MR imaging method according to an embodiment oranother embodiment of the present invention.

Referring to FIG. 8, aliasing artifacts are dispersed and shown in thefinal MR image 810 generated from K-space data for which under-samplinghas been performed at non-uniform intervals.

FIG. 9 is a diagram for describing an MR image generated by the MRIapparatus and the MR imaging method according to an embodiment oranother embodiment of the present invention.

In detail, FIG. 9 is an image obtained by performing image enhancementprocessing of a final MR image acquired by the MRI apparatus and the MRimaging method according to an embodiment or another embodiment of thepresent invention.

The MRI apparatus may restore under-sampled K-space data to generaterestored K-space data, and generate a final MR image by using therestored K-space data. Subsequently, the MRI apparatus may perform imageenhancement processing including noise reduction processing, edgeenhancement processing, and contrast enhancement processing, forimproving a quality of the final MR image.

When aliasing artifacts are dispersed and shown in an MR image, thealiasing artifacts are removed from the MR image through subsequentimage enhancement processing, thus minimizing aliasing artifacts.

FIG. 10 is diagrams for describing an improved quality of an MR imagegenerated by the MRI apparatus and method according to an embodiment oranother embodiment of the present invention.

A region 1010 of FIG. 10 shows a final MR image 1012 and under-sampledK-space data 1011 acquired by setting the same under-sampling intervalpattern for each block.

A region 1020 of FIG. 10 shows a final MR image 1022 and under-sampledK-space data 1021 acquired by less densely setting an under-samplinginterval pattern of at least one block included in an outer region of aK-space.

A region 1030 of FIG. 10 shows a final MR image 1032 and under-sampledK-space data 1031 acquired by more densely setting an under-samplinginterval pattern of a block disposed in a center region of the K-space.

A region 1040 of FIG. 10 shows a final MR image 1042 and under-sampledK-space data 1041 acquired by more densely setting under-samplinginterval patterns of four blocks disposed in the center region of theK-space.

Comparing the final MR images 1012, 1022, 1032, and 1042 of FIG. 10,aliasing artifacts existing in a final MR image are more reduced by moredensely setting under-sampling interval patterns of blocks disposed in alow-frequency domain of the K-space than those of blocks disposed in theother domain.

Moreover, in K-space data, a quality of an image may be adjusted byadjusting a size of a region or a block in which an under-samplinginterval pattern is more densely set. For example, a quality of an imageis more enhanced by increasing a size of a region or a block in which anunder-sampling interval pattern is more densely set. Also, a quality ofan image is degraded by decreasing a size of a region or a block inwhich an under-sampling interval pattern is more densely set. A qualityof an image may be adjusted by differently setting a non-uniformunder-sampling pattern depending on a part of an object to bephotographed. Also, in adjusting a quality of an image, a non-uniformunder-sampling pattern which is experimentally optimized is acquired foreach part of an object to be photographed, thereby enhancing a qualityof an image.

FIG. 11 is a diagram for describing an MRI apparatus 200 according toanother embodiment of the present invention.

In the MRI apparatus 200, the data acquirer 210 performs under-samplingon MR signals, respectively received from the plurality of channel coilsincluded in the RF multi-coil 205, at non-uniform intervals in a wholeK-space corresponding to a channel coil to acquire a plurality of piecesof line data.

The image processor 230 may restore a plurality of pieces of K-spacedata respectively corresponding to the plurality of channel coils on thebasis of a spatial correlation coefficient which is calculated by usingat least one of a plurality of pieces of line data acquired in a centerregion of a K-space and at least one of a plurality of pieces of linedata acquired in a peripheral region of the K-space.

Referring to FIG. 11, the data acquirer 210 may perform under-samplingat non-uniform intervals to acquire K-space data 1110 in a K-spacecorresponding to a channel coil. The under-sampled K-space data 1110, asillustrated, is obtained by performing under-sampling at overallnon-uniform intervals when a center region 1120 and peripheral regions1130 and 1140 are set to have different under-sampling intervals.

The data acquirer 210 may divide a K-space into a plurality of blocks,and set, as a calibration block, a first block which is disposed in acenter region of the K-space. In detail, the data acquirer 210 may set,as a calibration block 1120, a first block 1120 including n number oflines with respect to a center line of the K-space. For example, whenthe K-space includes 256 lines, the center line is a 128th line, and asan example, FIG. 11 illustrates a case in which the calibration block1120 includes five lines disposed adjacent to the 128th line that is thecenter line.

Moreover, FIG. 11 illustrates, for example, a case in which the K-spaceis divided into three blocks. However, the K-space may be divided intothree or more blocks, which may be set to have different under-samplinginterval patterns.

The data acquirer 210 may perform sampling on all lines in thecalibration block 1120 to acquire a plurality of pieces of calibrationline data. The data acquirer 210 may perform under-sampling at firstintervals to acquire a plurality of pieces of first line data in theK-space, and may additionally perform sampling on at least one secondline to further acquire at least one piece of second line data in thesecond blocks 1130 and 1140 disposed in the peripheral region of theK-space. FIG. 11 illustrates, for example, a case in which when aK-space corresponding to a channel coil is divided into a plurality ofblocks, and a block disposed in a center region of the K-space isdistinguished from a block disposed in a region other than the centerregion of the K-space. That is, the first block 1120 disposed in thecenter region of the K-space and the second blocks 1130 and 1140disposed in a region other than the center region of the K-space may beincluded in the K-space corresponding to the channel coil.

In detail, the data acquirer 210 may perform sampling on 126 to 130lines (which are all lines included in the calibration block 1120) toacquire a plurality of pieces of calibration line data. The dataacquirer 210 may perform under-sampling on the K-space at three-lineintervals, namely, perform under-sampling of an MR signal for each offirst, fourth, seventh, . . . , 3n+1st lines, to acquire a plurality ofpieces of first line data. The data acquirer 210 may additionallyperform under-sampling on a third or eighth line, which is at least oneline included in the peripheral regions 1130 and 1140 of the K-space, toacquire pieces of second line data 1151 and 1152.

The image processor 230 may acquire a spatial correlation coefficient byusing the plurality of pieces of calibration line data, the plurality ofpieces of first line data, and the at least one piece of second linedata.

In detail, in the example of FIG. 11, the image processor 230 mayacquire the spatial correlation coefficient by using the plurality ofpieces of line data (the calibration line data) included in the block1120, the plurality of pieces of first line data (which are acquired byperforming under-sampling of the MR signal for each of the first,fourth, seventh, . . . , 3n+1st lines), and the pieces of second linedata 1151 and 1152 which are acquired by additionally performingunder-sampling on the third or eighth line included in the peripheralregions 1130 and 1140 of the K-space. The image processor 230 mayperform calibration by using the acquired spatial correlationcoefficient to restore a plurality of pieces of unacquired line data inthe K-space.

FIG. 12 is a diagram for describing an operation of acquiring a spatialcorrelation coefficient in the MRI apparatus according to anotherembodiment of the present invention.

In acquiring a spatial correlation coefficient for performingcalibration, the image processor 230 may use a matrix operationsimilarly to FIG. 4A.

A left term 1210 of the matrix operation is composed of signal values ofa plurality of pieces of acquired line data, in under-sampled K-spacedata. A right term 1230 of the matrix operation is composed of signalvalues of calibration lines included in a calibration block, in theunder-sampled K-space data.

Moreover, the left term 1210 of the matrix operation may be composed ofsignal values 1221 of a plurality of pieces of line data, which areacquired by performing under-sampling on the K-space overall, and signalvalues 1222 of at least one of a plurality of pieces of line dataacquired in a peripheral region of the K-space. The right term 1230 maybe composed of signal values 1231 of a plurality of pieces ofcalibration line data acquired in a calibration block 1120. The imageprocessor 230 may perform an inverse operation of the matrix operationillustrated in FIG. 12 to calculate a spatial correlation coefficientKc.

By using the calculated spatial correlation coefficient Kc, the imageprocessor 230 may restore a plurality of pieces of unacquired line datain the K-space to the restored plurality of pieces of line data.

As described above, according to the one or more embodiments of thepresent invention, the MRI apparatus and method improve a quality of arestored MR image. In more detail, by acquiring K-space data throughunder-sampling performed at non-uniform under-sampling intervals, theMRI apparatus and method prevent a quality of an image from beingdegraded due to aliasing artifacts, thus improving a quality of arestored MR image.

Moreover, according to the one or more embodiments of the presentinvention, the MRI apparatus and method acquire K-space data throughunder-sampling, thus quickly acquiring an MR image. Also, the MRIapparatus and method quickly acquire an MR image with an improvedquality even without using an additional calibration signal used in aGRAPPA technique or using a coil sensitivity map that has additionalcoil information and is used in a SMASH technique.

Moreover, the MRI apparatus and the imaging method for the MRI apparatusaccording to one embodiment or another embodiment of the presentinvention under-samples a K-space in units of a block, and thus have amerit in restoring an image in a low frequency domain and a highfrequency domain which correspond to an entire region of the K-space.

The above-described embodiments may be written as computer programs andmay be implemented in general-use digital computers that execute theprograms using computer-readable recording media.

Examples of the computer-readable recording medium include magneticstorage media (e.g., ROM, floppy disks, hard disks, etc.) and opticalrecording media (e.g., CD-ROMs or DVDs).

It should be understood that the exemplary embodiments described thereinshould be considered in a descriptive sense only and not for purposes oflimitation. Descriptions of features or aspects within each embodimentshould typically be considered as available for other similar featuresor aspects in other embodiments.

While one or more embodiments of the present invention have beendescribed with reference to the figures, it will be understood by thoseof ordinary skill in the art that various changes in form and detailsmay be made therein without departing from the spirit and scope of thepresent invention as defined by the following claims.

1. A magnetic resonance imaging (MRI) apparatus comprising: a dataacquirer configured to under-sample MR signals, respectively receivedfrom a plurality of channel coils included in a radio frequency (RF)multi-coil, at non-uniform intervals to acquire pieces of line data; andan image processor configured to restore pieces of K-space datarespectively corresponding to the channel coils by using a positionalrelationship between the acquired pieces of line data in a K-space. 2.The MRI apparatus of claim 1, wherein the data acquirer is configured tounder-sample the MR signals at the non-uniform intervals in an entireK-space corresponding to one of the channel coils.
 3. The MRI apparatusof claim 1, wherein the data acquirer is configured to divide the Kspace corresponding to each of the channel coils into blocks, andunder-sample the MR signals for corresponding blocks at the non-uniformintervals to acquire the pieces of line data.
 4. The MRI apparatus ofclaim 3, wherein the data acquirer is configured to apply a samenon-uniform under-sampling pattern to the corresponding blocks, toacquire the pieces of line data.
 5. The MRI apparatus of claim 3,wherein the image processor is configured to restore pieces ofunacquired line data, by using the positional relationship between theacquired pieces of line data, and restore the K-space data correspondingto one of the channel coils, by using at least one of the restoredpieces of unacquired line data and the acquired pieces of line data. 6.The MRI apparatus of claim 5, wherein the image processor is configuredto set one of the acquired pieces of line data as a reference line data,and determine the positional relationship, as a first relationship,based on spatial distances between the reference line data and at leasttwo pieces of the acquired pieces of line data.
 7. The MRI apparatus ofclaim 6, wherein the image processor is configured to restore one of thepieces of unacquired line data having the first relationship with the atleast two pieces of the acquired pieces of line data, and restoreanother one of the pieces of unacquired line data having the firstrelationship with at least one of the restored pieces of line data andat least one of the acquired pieces of line data.
 8. The MRI apparatusof claim 6, wherein the image processor is configured to calculate aspatial correlation coefficient corresponding to the first relationship,by using the reference line data and the at least two pieces of theacquired pieces of line data, and to restore the pieces of unacquiredline data by using the calculated spatial correlation coefficient. 9.The MRI apparatus of claim 5, wherein the image processor is configuredto sequentially restore the pieces of unacquired line data, included ineach of the blocks, in a predetermined order.
 10. The MRI apparatus ofclaim 3, wherein the data acquirer is configured to apply differentnon-uniform under-sampling patterns to the corresponding blocks, toacquire the pieces of line data.
 11. The MRI apparatus of claim 3,wherein the blocks are divided into of groups, and the groups comprisedifferent under-sampling interval patterns.
 12. The MRI apparatus ofclaim 3, wherein the blocks comprise a first block and a second block,and the first and second blocks comprise under-sampling intervalpatterns different from one another.
 13. The MRI apparatus of claim 12,wherein the first block comprises more or fewer pieces of the acquiredpieces of line data than the second block.
 14. The MRI apparatus ofclaim 3, wherein the blocks comprise a first block, a second block, anda third block, the second block comprises fewer pieces of the acquiredpieces of line data than the first block, and the third block comprisesmore pieces of the acquired pieces of line data than the first block.15. The MRI apparatus of claim 14, wherein the third block is disposedcloser to a center line of the K-space than the first and second blocks.16. The MRI apparatus of claim 1, wherein the image processor isconfigured to calculate a spatial transform on the restored pieces ofK-space data to generate MR images by channel, and acquire a final MRimage by using the MR images by channel.
 17. The MRI apparatus of claim16, wherein the image processor is configured to calculate an inverseFourier transform on the restored pieces of K-space data to generate theMR images by channel.
 18. The MRI apparatus of claim 16, wherein theimage processor is configured to calculate a sum of squares or a complexsum of the MR images by channel to generate the final MR image.
 19. TheMRI apparatus of claim 3, wherein a number of the blocks and sizes ofthe blocks are set based on at least one a hardware type of the RFmulti-coil and a region of an object to be imaged.
 20. The MRI apparatusof claim 3, wherein the data acquirer is configured to apply anon-uniform under-sampling pattern, to acquire the pieces of line data,which is set based on at least one of a hardware type of the RFmulti-coil and a region of an object to be imaged.
 21. A magneticresonance imaging (MRI) apparatus comprising: a data acquirer configuredto under-sample MR signals, respectively received from channel coilsincluded in a radio frequency (RF) multi-coil, at non-uniform intervalsin an entire K-space corresponding to one of the channel coils toacquire pieces of line data; and an image processor configured torestore pieces of K-space data respectively corresponding to the channelcoils based on a spatial correlation coefficient which is calculated byusing at least one of the pieces of line data acquired in a centerregion of the K-space and at least one of the pieces of line dataacquired in a peripheral region of the K-space.
 22. The MRI apparatus ofclaim 21, wherein the data acquirer is configured to divide the K-spaceinto blocks, and set, as a calibration block, a first block which isdisposed in the center region of the K-space.
 23. The MRI apparatus ofclaim 22, wherein the data acquirer is configured to sample all lines inthe calibration block, to acquire pieces of calibration line data, tounder-sample the MR signals at first intervals to acquire pieces offirst line data in the whole entire K-space, and to additionally atleast one second line to further acquire at least one piece of secondline data in a second block disposed in the peripheral region of theK-space.
 24. The MRI apparatus of claim 23, wherein the image processoris configured to acquire the spatial correlation coefficient by usingthe pieces of calibration line data, the pieces of first line data, andthe at least one piece of second line data.
 25. A magnetic resonanceimaging (MRI) apparatus comprising: a data acquirer configured tounder-sample MR signals, which are respectively received from channelcoils included in a radio frequency (RF) multi-coil, at non-uniformintervals in an entire K-space of corresponding channel coils to acquirepieces of line data; and an image processor is configured to restorecomplete K-space data corresponding to each of the channel coils byusing a K-space positional relationship between the pieces of line datawhich are acquired by under-sampling at non-uniform intervals.
 26. Amagnetic resonance (MR) imaging method using a radio frequency (RF)multi-coil including channel coils, the method comprising:under-sampling MR signals, respectively received from the channel coils,at non-uniform intervals to acquire pieces of line data; and restoringpieces of K-space data respectively corresponding to the channel coilsby using a positional relationship between the acquired pieces of linedata in a K-space.
 27. The method of claim 26, wherein theunder-sampling comprises: under-sampling the MR signals at thenon-uniform intervals in an entire K-space of corresponding channelcoils to acquire the pieces of line data.
 28. The method of claim 26,wherein the under-sampling comprises: dividing the K-space of thecorresponding channel coils into blocks; and under-sampling the MRsignals for the blocks at the non-uniform intervals to acquire thepieces of line data.
 29. The method of claim 28, wherein theunder-sampling further comprises: applying a same non-uniformunder-sampling pattern to the corresponding blocks.
 30. The method ofclaim 28, wherein the restoring the pieces of K-space data comprises:restoring pieces of unacquired line data by using the positionalrelationship between the acquired pieces of line data; and restoringK-space data corresponding to one of the channel coils by using one ofthe restored pieces of line data and the acquired pieces of line data.